PBPK Modelling: Current Approaches and Health Applications

Physiologically based pharmacokinetic (PBPK) modeling is a tool for predicting the absorption, distribution, metabolism, and excretion of chemicals in the body. By integrating physiological, biochemical, and anatomical data, these models provide insight into how substances move through tissues and organs. PBPK models are widely used in drug development, toxicology, and regulatory science to assess exposure risks and optimize dosing strategies.

Advancements in computational power and data availability have improved PBPK models, increasing their accuracy and applicability. Researchers continue refining these models by incorporating diverse biological parameters and datasets.

Core Model Components

PBPK models represent the body as interconnected compartments simulating physiological processes governing chemical movement. Each compartment corresponds to a tissue or organ, with parameters defining absorption, transport, metabolism, and elimination. Model accuracy depends on how well biological characteristics, such as tissue composition, blood perfusion rates, and chemical properties, are incorporated.

Tissue Compartments

PBPK models divide the body into compartments reflecting anatomical and physiological properties that influence chemical distribution. Common compartments include the liver, kidneys, lungs, fat, muscle, and brain, each playing a role in drug metabolism and clearance. The liver, for instance, is a primary site for biotransformation due to its high concentration of metabolic enzymes, while adipose tissue serves as a reservoir for lipophilic compounds.

Compartment selection depends on the model’s intended use. A drug-focused PBPK model may emphasize metabolically active tissues, whereas an environmental toxicology model might prioritize storage and elimination pathways. Studies in Regulatory Toxicology and Pharmacology (2022) highlight the importance of optimizing compartment selection based on a compound’s physicochemical properties to improve predictive accuracy.

Blood Flow

Blood flow dynamics determine how chemicals are delivered to tissues. Highly perfused organs, such as the liver and kidneys, receive significant portions of cardiac output, influencing systemic clearance rates. Poorly perfused tissues, like adipose and bone, accumulate chemicals more slowly, affecting long-term distribution and retention.

Blood flow rates are typically derived from physiological databases such as the International Commission on Radiological Protection (ICRP) reference values, which provide standardized perfusion data across species and populations. Factors such as age, sex, and disease-related alterations in circulation further refine predictions. Clinical pharmacokinetic modeling studies in The Journal of Pharmacokinetics and Pharmacodynamics (2023) demonstrate the importance of capturing these dynamics for evaluating drug efficacy and toxicity.

Partition Coefficients

Partition coefficients determine how a chemical distributes between blood and tissues, influencing accumulation and elimination. These values depend on factors such as tissue composition, lipid content, and protein binding affinity. Experimental methods, including equilibrium dialysis and in vitro tissue homogenates, determine partitioning behavior, while computational approaches such as quantitative structure-activity relationships (QSAR) predict coefficients for novel compounds.

Errors in partition coefficient estimation can lead to incorrect predictions of tissue exposure. Advances in machine learning-based prediction models, as discussed in Frontiers in Pharmacology (2023), have improved partition coefficient estimations, enabling more precise PBPK simulations that inform drug dosing strategies and risk assessments.

Mathematical Foundations

PBPK models use mathematical frameworks to describe chemical movement through biological systems. Systems of differential equations govern the rates of absorption, distribution, metabolism, and excretion (ADME). Each equation represents mass balance constraints within a tissue compartment, ensuring the total amount of a substance entering, residing in, and leaving a compartment is accounted for over time.

These models typically use ordinary differential equations (ODEs), where each equation describes the rate of change in chemical concentration within a specific compartment. Perfusion-limited models assume blood flow primarily governs chemical exchange, leading to first-order kinetics that simplify computations. Permeability-limited models account for additional resistance at cellular membranes, requiring more complex equations incorporating diffusion coefficients and transporter kinetics. The choice of model formulation depends on the physicochemical properties of the substance, as highlighted in The Journal of Pharmacokinetics and Pharmacodynamics (2023), which examines how permeability constraints influence drug distribution in tissues with tight junctions, such as the blood-brain barrier.

Parameter selection requires physiological inputs, such as organ volumes, blood flow rates, and metabolic clearance values, sourced from experimental studies, population databases, or in silico predictions. Sensitivity analyses assess how variations in parameters impact model outcomes, helping researchers identify key factors influencing tissue exposure predictions. Global sensitivity analysis techniques, such as Sobol or Morris methods, provide systematic approaches for ranking parameter importance, as documented in CPT: Pharmacometrics & Systems Pharmacology (2022).

Solving these differential equations requires numerical integration techniques, as analytical solutions are rarely feasible for multi-compartment models. Common solvers include Runge-Kutta methods and stiff solvers like backward differentiation formulas (BDF), which handle rapid concentration changes effectively. Computational efficiency is crucial for scaling PBPK models to population-level simulations or incorporating stochastic variability. Advances in parallel computing and machine learning-based surrogate modeling, as explored in Computational and Structural Biotechnology Journal (2023), have enhanced PBPK simulation speed and scalability, enabling real-time predictions for clinical and regulatory applications.

Parameter Estimation Approaches

Accurate parameter estimation is essential for reliable PBPK model predictions. Parameters such as metabolic clearance rates, tissue-binding affinities, and transport kinetics are derived from experimental data, in silico predictions, and statistical inference methods.

In vitro assays provide critical insights into enzyme kinetics, plasma protein binding, and cellular uptake mechanisms. Techniques such as microsomal stability assays and hepatocyte incubations quantify metabolic clearance, while equilibrium dialysis and ultrafiltration studies characterize protein-drug interactions. These laboratory-derived values must be appropriately scaled to whole-body physiology using methods like allometric scaling and hepatocyte-to-liver extrapolation.

Computational techniques, including Bayesian inference and Markov chain Monte Carlo (MCMC) methods, estimate parameter distributions rather than fixed values, incorporating variability and uncertainty into the model. Population-based modeling techniques, such as nonlinear mixed-effects modeling (NLME), further refine estimations by accounting for demographic and physiological diversity. These methods improve PBPK models for special populations, including pediatric and geriatric groups, where physiological parameters differ significantly from standard adult values.

Machine learning and artificial intelligence (AI) have emerged as tools for parameter optimization. Neural networks and ensemble learning algorithms analyze large datasets to identify correlations between chemical properties and pharmacokinetic behaviors, generating predictive models that inform parameter selection. Advances in deep learning now enable automated feature extraction from high-throughput screening data, streamlining the identification of physiologically relevant parameters. While promising, these approaches require validation against experimental and clinical benchmarks to ensure biological plausibility.

Data Sources For Model Inputs

Accurate input parameters are fundamental to reliable PBPK models. Physiological parameters, such as organ volumes and blood flow rates, are often derived from databases like the International Commission on Radiological Protection (ICRP) reference values and the National Health and Nutrition Examination Survey (NHANES). These resources offer population-specific data, allowing for adjustments based on age, sex, and disease state. Pharmacokinetic parameters are also available from regulatory guidelines issued by the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA).

Experimental studies further refine model accuracy. Human and animal studies measuring plasma and tissue concentrations following drug administration provide direct input values for model calibration. Techniques such as positron emission tomography (PET) imaging allow real-time tracking of compound distribution, offering spatial and temporal insights that enhance predictive capabilities. In vitro studies using liver microsomes, hepatocytes, or recombinant enzymes generate enzyme kinetic data critical for characterizing metabolic pathways. These experimentally obtained parameters must be carefully scaled to whole-body models using physiologically relevant extrapolation techniques.

In silico methods expand data acquisition options, particularly for novel compounds where empirical data are limited. Quantitative structure-activity relationship (QSAR) models estimate physicochemical properties such as lipophilicity and protein binding, which influence partitioning behavior. Physiologically based biopharmaceutical modeling (PBBM) integrates dissolution, permeability, and solubility data to refine oral drug absorption predictions, a method increasingly recognized by regulatory agencies for bioequivalence assessments. Machine learning-driven approaches enhance predictive accuracy by identifying complex relationships between molecular descriptors and pharmacokinetic behavior, though validation against experimental data remains a priority.